Bayesian hierarchical hypothesis testing in large-scale genome-wide association analysis.

ABSTRACT: Variable selection and large-scale hypothesis testing are techniques commonly used to analyze high-dimensional genomic data. Despite recent advances in theory and methodology, variable selection and inference with highly collinear features remain challenging. For instance, collinearity poses a great challenge in genome-wide association studies involving millions of variants, many of which may be in high linkage disequilibrium. In such settings, collinearity can significantly reduce the power of variable selection methods to identify individual variants associated with an outcome. To address such challenges, we developed a Bayesian hierarchical hypothesis testing (BHHT)-a novel multiresolution testing procedure that offers high power with adequate error control and fine-mapping resolution.

SUBMITTER: Samaddar A 

PROVIDER: S-EPMC11631447 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature